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Acutance an objective measure of retinal nerve fibre image clarity

机译:警惕一种客观测量视网膜神经纤维图像清晰度的方法

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摘要

>Background/aims: The interpretation of high contrast retinal nerve fibre layer (RNFL) images in glaucoma can be confounded by the presence of image blur; it can be difficult to discern diffuse axon loss in a poor quality image. One solution is to provide an objective measure of the image quality based on features in the image other than the RNFL. In this study the authors have developed an objective method to quantify the clarity of RNFL images, comparing it with a subjective image grading system.>Methods: Digitally acquired, monochrome retinal images were taken from 58 eyes (one image per eye) with a Topcon 50 IX retinal camera. Image resolution was 1320 × 1032 pixels at 8 bits per pixel. Image sharpness was subjectively graded by two masked experienced observers on a scale 1 to 5 relative to a reference set of RNFL images. Software algorithms were developed using Matlab (5.2) to calculate the acutance, an objective measure of the physical characteristics that underlie the subjective impression of sharpness in an image.>Results: Acutance values could be calculated for all the images. The Pearson correlation coefficients of the log of the acutance for each image and the subjective grades of observer 1 and observer 2 were 0.90 (p<0.001, n=58) and 0.84 (p<0.001, n=58) respectively.>Conclusions: These data suggest that acutance may provide a useful objective measure of image quality, which correlates well with the subjective impression of the digital retinal image sharpness. Objective measures of image quality should help in the discrimination of diffuse retinal nerve fibre loss from image blur in patients with diffuse glaucomatous damage.
机译:>背景/目的:青光眼中高对比度视网膜神经纤维层(RNFL)图像的解释可能会因图像模糊而混淆;很难分辨出质量差的图像中的弥漫性轴突损失。一种解决方案是基于除RNFL以外的图像特征提供客观的图像质量度量。在这项研究中,作者开发了一种客观的方法来量化RNFL图像的清晰度,并将其与主观图像分级系统进行比较。>方法:数字采集的单色视网膜图像是从58只眼中拍摄的(一张图像每只眼睛)和Topcon 50 IX视网膜相机。图像分辨率为1320×1032像素,每像素8位。图像清晰度是由两个蒙面的经验丰富的观察者主观地相对于RNFL图像参考集以1到5的比例进行分级的。使用Matlab(5.2)开发了软件算法来计算清晰度,这是对物理特性的客观度量,这些物理特性是主观图像清晰度的基础。>结果:可以为所有图像计算清晰度值。每个图像的清晰度对数的皮尔逊相关系数以及观察者1和观察者2的主观等级分别为0.90(p <0.001,n = 58)和0.84(p <0.001,n = 58)。结论:这些数据表明,锐视度可以提供一种有用的客观的图像质量度量,它与数字视网膜图像清晰度的主观印象密切相关。图像质量的客观测量应有助于从弥散性青光眼损害患者中识别出弥散性视网膜神经纤维丢失和图像模糊。

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